Multi-channel content based image retrieval method for skin diseases using similarity network fusion and deep community analysis. (September 2022)

• Record Type: Journal Article
• Authors: Wang, Yuheng; Fariah Haq, Nandinee; Cai, Jiayue; Kalia, Sunil; Lui, Harvey; Jane Wang, Z.; Lee, Tim K.
• Is Part Of: Biomedical signal processing and control. Volume 78(2022)
• Publication Date: 2022-09
• Subjects: Skin cancer -- Image retrieval -- Multi-channel -- Deep community -- Graph analysis; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103893
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A new method for skin lesion image retrieval using multiple input channels is proposed. Similarity network fusion solves the problem of dimensional imbalance among inputs. Community-based search technique helps find strongly connected subjects within similarity networks. The technique is applicable to complex clinical scenarios and produces state-of-the-art performance. Abstract: Content-based image retrieval (CBIR) could be an efficient diagnostic tool. Physicians could consult a CBIR system before making a diagnosis for a clinical case by retrieving a set of images with similar appearance and pathological diagnosis from a data archive. With access to various imaging modalities, physicians may want to match more than one image modality and non-image information. How to make full use of this diverse information is an important research question. In this paper, we propose a CBIR framework for skin diseases that incorporates multi-sourced information including dermoscopic images, clinical images, and meta information. The proposed framework fuses the multi-sourced features in mutual similarity level; thus, solving severe dimensional bias problems for image and non-image information. We then utilize a graph-based community analysis on similarity networks where similar images are strongly connected and help retrieve similar images with improved performance. Evaluations were carried out using two well-known skin datasets EDRA and ISIC 2019. The carefully designed framework demonstrates a substantial improvement in finding similar cases for different skin diseases with an average precision of 0.836, which is the state-of-the-art performance for retrieving skin disease types. In addition, the proposed framework is also applicable to scenarios with a single typed feature with improved performance. By integrating multi-sourced information from the same patient, the proposed CBIR system could be potentially used in complex clinical scenarios with a trustable performance benefitting from both abundant information and advanced community search technique.